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The increasing popularity and adoption of the Internet of Things (IoT) ecosystem in various domains has brought attention to the security breaches linked with this paradigm. As the number of IoT devices continues to grow, it is essential to ensure that they are secured to protect against potential threats and attacks. IoT network proliferation of interconnected devices has significantly raised security concerns making them attractive targets for cyber attackers seeking to gain unauthorized access to systems and cause disruptions. As IoT networks collect and transmit sensitive data using centralized architecture, ensuring security and integrity of these networks becomes paramount. Distributed Ledger Technology (DLT) has emerged as a promising solution for enhancing IoT security. Two prominent DLT platforms: Blockchain and Internet of Things Application (IOTA) technologies can provide a more secure and resilient foundation for IoT ecosystems, and also help to mitigate risks associated with central node vulnerabilities. DLT-based IoT systems can also enable the creation of decentralized marketplaces and autonomous agents that can operate without human intervention. The objective of this research is to offer a comprehensive as well as fundamental study of IoT ecosystems and its associated security risks. Moreover, this paper provides a holistic study of the DLT platform and bibliometric inspection using VoS viewer tool on generic DLT platform technologies i.e., Blockchain and IOTA for securing data in IoT ecosystem. By leveraging bibliometric insights resulting from both DLT technologies, this study identities the most promising areas for further investigation and contribute to advancing security in IoT ecosystems. This survey contributes to the ongoing discourse on IoT security by providing a thorough comprehensive comparison of DLT solutions i.e., Blockchain and IOTA technologies on various key metrics, revealing that IOTA technology is projected to offer significant improvements over blockchain in securing sustainable IoT ecosystems. [ABSTRACT FROM AUTHOR]

Mandelbrot set, which was provided as a highlight in fractal and chaos, is studied by many researchers. With the extension of Mandelbrot set to generalized M set with different kinds of exponent k (k − M set), properties are hard to understand when k is a complex number. In this paper, fractal property of generalized M set with complex exponent z is studied. First, a relation is constructed between generalized M set with complex and real exponent. Then, distribution of z − M set on complex plane is researched. Meanwhile, symmetry of generalized M set is proved. Finally, graphics, generated by escape time algorithm, are the validated results of this paper. [ABSTRACT FROM AUTHOR]

The usage of the Internet of Things (IoT) in the field of transportation appears to have immense potential. Intelligent vehicle systems can exchange seamless information to assist cars to ensure better traffic control and road safety. The dynamic topology of this network, connecting a large number of vehicles, makes it vulnerable to several threats like authentication, data integrity, confidentiality, etc. These threats jeopardize the safety of vehicles, riders, and the entire system. Researchers are developing several approaches to combat security threats in connected and autonomous vehicles. Artificial Intelligence is being used by both scientists and hackers for protecting and attacking the networks, respectively. Nevertheless, wirelessly coupled cars on the network are in constant peril. This motivated us to develop an intrusion detection model that can be run in low-end devices with low processing and memory capacity and can prevent security threats and protect the connected vehicle network. This research paper presents an Attention-enabled Hierarchical Deep Neural Network (AHDNN) as a solution to detect intrusion and ensure autonomous vehicles' security both at the nodes and at the network level. The proposed AHDNN framework has a very low false negative rate of 0.012 ensuring a very low rate of missing an intrusion in normal communication. This enables enhanced security in vehicular networks. [ABSTRACT FROM AUTHOR]

Centralized Personal Health Records (PHR) are mutable with compromised security as it may lead to a single point of failure. Confidentiality, protection and security are the common issues in clinical record frameworks. Specific security and protection schemes are being used to secure clinical records. Accordingly, using the Interplanetary File System (IPFS), a decentralized PHR can be maintained to allow patients to access their records without delay. Moreover, a Kademlia-based distributed hash table provides fault tolerance and enables patients to keep track of their medical history. However, a significant issue in IPFS is data availability. It is only available on the web until users or hosts of the network request each peer, later it leads to a permanent loss of data. We propose an architecture that aims to provide faster retrieval and constant PHR availability using Blockchain and IPFS. The results show that an optimal node is selected in each iteration amongst all the available adjacent nodes. [ABSTRACT FROM AUTHOR]

In 2017, the Government of India launched the goods and services tax (GST), referred to as "one tax, one nation, one market". This tax all Indian businesses are subject to this tax. GST was framed with the objective of bringing tax handling for all businesses onto a single platform and developing a transparent and effective system in which all businesses will pay taxes. This paper identifies and addresses GST implementation challenges and proposes a solution, GSTChain, using blockchain network technology. Currently, GST is collected at the sellers end and bifurcated between the Indian state and central governments. GSTChain is a blockchain system based on trust and autonomy with the objective of making taxpayers' lives easy and tax collection efficient and transparent for the government. [ABSTRACT FROM AUTHOR]

The creation of digital marketing has enabled companies to adopt personalized item recommendations for their customers. This process keeps them ahead of the competition. One of the techniques used in item recommendation is known as item-based recommendation system or item–item collaborative filtering. Presently, item recommendation is based completely on ratings like 1–5, which is not included in the comment section. In this context, users or customers express their feelings and thoughts about products or services. This paper proposes a machine learning model system where 0, 2, 4 are used to rate products. 0 is negative, 2 is neutral, 4 is positive. This will be in addition to the existing review system that takes care of the users' reviews and comments, without disrupting it. We have implemented this model by using Keras, Pandas and Sci-kit Learning libraries to run the internal work. The proposed approach improved prediction with 7 9 % accuracy for Yelp datasets of businesses across 11 metropolitan areas in four countries, along with a mean absolute error (MAE) of 2 1 % , precision at 7 9 % , recall at 8 0 % and F1-Score at 7 9 %. Our model shows scalability advantage and how organizations can revolutionize their recommender systems to attract possible customers and increase patronage. Also, the proposed similarity algorithm was compared to conventional algorithms to estimate its performance and accuracy in terms of its root mean square error (RMSE), precision and recall. Results of this experiment indicate that the similarity recommendation algorithm performs better than the conventional algorithm and enhances recommendation accuracy. [ABSTRACT FROM AUTHOR]

In today's world, one of the most severe attacks that wireless sensor networks (WSNs) face is a Black-Hole (BH) attack which is a type of Denial of Service (DoS) attack. This attack blocks data and injects infected programs into a set of sensors in a group to capture packets before reached to the target. Therefore, raw data in the BH region is thwarted and is unable to reach its destination. The network is susceptible to various types of attacks as it is accessible to all types of users and minimizing the energy depletion without compromising the network lifetime is an NP-hard problem. Even though numerous protocols came into effect to overcome the BH attack and to enhance the security of packet delivery in WSNs, Simulated Annealing Black-hole attack Detection (SABD) based Enhanced Gravitational Search Algorithm (EGSA) is yet another implemented strategy to reduce the BH attacks. EGSA-SABD detects and isolates the BH infectors in WSNs. Initially, sensor nodes are hierarchically clustered using similar residual energy to reduce energy consumption. Then, the BH attack possibility in a deployed node is evaluated to find the existence of BH nodes in the region. In the end, EGSA-SABD is employed to detect and quarantine BH attackers in WSNs. The performance of EGSA-SABD is evaluated with certain metrics such as BH attack detection probability rate (BHatt_Prate), energy consumption (Ec), Duration of BH attack detection (Attduration), Packet delivery ratio (Pdr). Based on the experimental observations, the EGSA-SABD outperforms the BHatt_Prate by 13% and also reduces the energy consumption by 21%. [ABSTRACT FROM AUTHOR]

In recommender systems, Collaborative Filtering (CF) plays an essential role in promoting recommendation services. The conventional CF approach has limitations, namely data sparsity and cold-start. The matrix decomposition approach is demonstrated to be one of the effective approaches used in developing recommendation systems. This paper presents a new approach that uses CF and Singular Value Decomposition (SVD) + + for implementing a recommendation system. Therefore, this work is an attempt to extend the existing recommendation systems by (i) finding similarity between user and item from rating matrices using cosine similarity; (ii) predicting missing ratings using a matrix decomposition approach, and (iii) recommending top-N user-preferred items. The recommender system's performance is evaluated considering Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). Performance evaluation is accomplished by comparing the systems developed using CF in combination with six different algorithms, namely SVD, SVD + + , Co-Clustering, KNNBasic, KNNBaseline, and KNNWithMeans. We have experimented using MovieLens 100 K, MovieLens 1 M, and BookCrossing datasets. The results prove that the proposed approach gives a lesser error rate when cross-validation (CV = { 5 , 1 0 , 1 5 }) is performed. The experimental results show that the lowest error rate is achieved with MovieLens 100 K dataset (RMSE = 0. 9 1 2 3 , MAE = 0. 7 1 4 9). The proposed approach also alleviates the sparsity and cold-start problems and recommends the relevant items. [ABSTRACT FROM AUTHOR]

Decision making remains a prominent issue in all the problem domains. To make better decisions, multiple factors of the given problem need to be considered and evaluated. Multi-criteria decision-making methods have been used popularly for solving decision-making problems characterized by multiple factors. When multiple factors are considered, it is recommended to categorize the factors into the main criteria and sub-criteria. In this paper, GRAP-an integrated ranking algorithm has been developed by combining Grey Relational Analysis, Rank Sum, and Preference Ranking Organization Method Enrichment Evaluation methods (PROMETHEE) to solve decision-making problems. The weights of the sub-criteria are calculated using the Rank Sum method. Grey Relational Analysis method is used to convert the sub-criteria values into main criteria values in the form of evaluation scores of alternatives. The final ranking scores of the alternatives are obtained using the PROMETHEE method. A decision model is developed using the proposed GRAP algorithm and applied to the Job Profile selection case study. The developed decision model showed much better results compared to other MCDM approaches namely the Simple Additive Weight method, TOPSIS, VIKOR, and Complex Proportional Assessment (COPRAS). Further, a sanity check has been carried out by comparing the results of the decision model with experts' opinions. [ABSTRACT FROM AUTHOR]